智能系统学报2016，Vol.11Issue(1)：93-98,6.DOI:10.11992.tis.201410021
一种改进的自适应快速AF-DBSCAN聚类算法
An improved adaptive and fast AF-DBSCAN clustering algorithm
摘要
Abstract
The density⁃based DBSCAN clustering algorithm can identify clusters with arbitrary shape, however, the choice of the global parameters Eps and MinPts requires manual intervention, the process of regional query is com⁃plex and loses objects easily. Therefore, an improved density clustering algorithm with adaptive parameter for fast regional queries is proposed. Using KNN distribution and mathematical statistical analysis, the optimal global pa⁃rameters Eps and MinPts are adaptively calculated, so as to avoid manual intervention and enable full automation of the clustering process. The regional query is conducted by improving the selection manner of the object, which is represented by a seed and thus avoiding manual intervention, and so the clustering efficiency is effectively in⁃creased. The experiment results looking at density clustering of four typical data sets show that the proposed method effectively improves clustering accuracy by 8.825% and reduces the average time of clustering by 0.92 s.关键词
密度聚类/DBSCAN/区域查询/全局参数/KNN分布/数学统计分析Key words
density clustering/DBSCAN/region query/global parameters/KNN distribution/mathematical statis-tics and analysis分类
信息技术与安全科学引用本文复制引用
周治平,王杰锋,朱书伟,孙子文..一种改进的自适应快速AF-DBSCAN聚类算法[J].智能系统学报,2016,11(1):93-98,6.基金项目
国家自然科学基金资助项目（61373126）；江苏省产学研联合创新资金－前瞻性联合研究基金资助项目（ BY2013015-33）． （）
